Course Strategy
How to Stand Out in World of AI Content
Large Language Models have drastically lowered the level of effort required to create large amounts of pretty good course content. This is especially true in topic areas that are already broadly covered and from a beginner to intermediate level. This makes sense - the LLMs were trained on this content after all.
And when I say "pretty good"... What I really mean is damn good. Good enough that I would personally have no problem trusting current frontier models to explain most any technical topic to me (with a bit of double checking of course).
Can you spot the AI?
To get a sense for this, check out the two blurbs below. One was written by a leading expert in his field, another by OpenAI's GPT-5.2 model.
Blurb A
Golden Ticket overview
The Golden Ticket attack is a Kerberos abuse technique in Active Directory where an attacker forges a valid Kerberos Ticket Granting Ticket (TGT). To do this, the attacker must obtain the NTLM hash of the KRBTGT account, which is the secret key used by the domain to sign all Kerberos tickets. With this hash, the attacker can cryptographically generate their own TGTs that the domain will fully trust, without ever contacting a domain controller for authentication.
Once a Golden Ticket is created, the attacker can impersonate any user, including Domain Admins, and set arbitrary properties such as group memberships and ticket lifetime. Because Kerberos only validates the ticket signature (which the attacker can now generate correctly), these forged tickets are accepted as legitimate. This makes Golden Tickets especially dangerous: they provide long-term, stealthy persistence, often surviving password resets and reboots, and can only be fully invalidated by rotating the KRBTGT password twice across the domain.
Author: Hidden — click to reveal
Blurb B
Golden Ticket implications
Once an attacker has compromised the domain, they can perform a Golden Ticket attack. This is a powerful persistence and lateral movement technique that relies on the compromise of Kerberos's most critical key, the password hash of KRBTGT. Armed with this password hash, attackers can forge their own TGTs with elevated permissions. This provides the attacker with access to nearly everything in the domain.
An attacker can also steal the tickets from another user. The tickets are not modified and provide the attacker with access as that user. If the attacker steals a service ticket they can access that specific service, and only that service, as the original user. If the attacker steals the TGT, they can access any service that the user can access.
Author: Hidden — click to reveal
Tough to tell, right? Even if you can spot the AI writing style, both descriptions are accurate and well written.
Special thanks to Tim Medin and the folks at Red Siege for letting me include this blurb from their pay-what-you-can Kerberos Workshop! Tim and team are fantastic and have also written some amazing course content with hands-on labs for you at https://training.redsiege.com/. Skip the hours fighting with the AIs and configuring your own lab setup and go buy their courses!
This presents a tough problem as a course creator. How do you make your courses stand out as unique and valuable in a world where anyone can publish damn good content on highly technical topics without any prior expertise?
The answer lies in two tenets that are core to why we're building CourseStack.
- • Students look to trusted experts to guide them through the learning process.
- • Technical expertise can only be achieved through realistic, hands-on learning.
Become a Trusted Expert
Admittedly, becoming a trusted expert is really tough. It takes years of experience in your field building authority through open source contributions, blogging, conference talks, and more.
Building community is also an effective way to gain trust, and engage with your students at the same time! Keep an eye out for Tyler Ramsbey's upcoming guest article on How to Build an Engaged Community.
There are no shortcuts here, but putting in the work over time can compound into a huge advantage over someone fresh in the field relying heavily on LLMs to help build their content. Luckily, there is something a bit more tactical you can do to have a more immediate impact — building great hands-on labs, challenges, and exercises within your course content.
Build Hands-On Learning
This is where an AI-generated course falls flat compared to a course thoughtfully designed and built by a human expert. LLMs are excellent at explaining what something is and why it matters, but they can't reliably deliver a great lab experience where the student interacts with the content.
There are a few reasons for this. First, LLMs don't have access to the supporting infrastructure required to deploy real labs to students. Spinning up virtual machines, configuring networks, managing credentials, isolating environments, resetting state, handling scale, and cleaning everything up afterward isn't something a prompt can solve on its own.
Without real infrastructure behind the content, "hands-on" often just means screenshots, copy-paste commands, or theoretical walkthroughs. We see this with a lot of courses outside of the CourseStack platform that claim to be "hands-on".
A video walkthrough of someone else doing the work is NOT hands-on. That's entertainment for the student. It's the difference between watching someone else play a video game or playing one yourself.
A second issue AI models run into when building lab setups will be familiar to anyone who has tried to fully automate a realistic lab environment... something almost works, but not quite. A version mismatch, update, or some other oddity specific to your setup breaks at the last minute.
It's tough to figure out these errors, and the more complex your setup the more likely you are to run into edge cases that the LLM can't quite handle. This is why hands-on learning remains such a powerful differentiator for successful course creators. This type of content elevates your course above the slop, and truly educates the student better.
Great labs are hard to build. They take time, testing, iteration, and expertise. That difficulty makes them hard to replicate with AI alone.
When students can interact with real systems, break things, fix them, and see the consequences firsthand, they aren't just consuming content. They're building intuition, confidence, and real-world skill.
Ready to build your own academy?
Start creating hands-on technical courses with CourseStack today. It's free to get started.